Duck 2

What Duck 2 Actually Is

Duck 2 is a lightweight duck detection and classification framework used primarily in wildlife monitoring and agricultural pest management. It builds on top of earlier models but improves inference speed significantly. The core idea is straightforward: feed it images from trail cameras or drone footage, and it spits out species-level classifications along with confidence scores. I started using it about two years ago when our research team needed something faster than the standard YOLO-based pipelines we'd been running. The previous setup took roughly 40 seconds per frame on our hardware. Duck 2 brought that down to around 800 milliseconds on the same machine. That alone made real-time processing viable for our drone surveys.

How Duck 2 Works Under the Hood

It uses a modified EfficientDet backbone with a custom feature pyramid that's been pruned for edge deployment. The training data covers 14 waterfowl species commonly found across North America and Europe, plus a catch-all "unknown bird" class that handles outliers. You can retrain it on your own dataset, but the default weights cover the common species reasonably well out of the box. The export pipeline supports ONNX, TensorRT, and CoreML. I tend to use TensorRT for GPU deployments and CoreML when pushing to iOS devices in the field. The model size varies depending on which variant you pick — the tiny version is about 12MB, the base is roughly 45MB, and the large variant sits around 120MB. For most field work, the base variant hits the sweet spot between accuracy and speed.

Setting It Up

The installation is fairly painless if you're working in Python 3.9 or later. Clone the repo, create a virtual environment, and run pip install from the requirements.txt file. The tricky part isn't the install — it's getting your input pipeline shaped correctly. Images need to be preprocessed to 640x640 resolution before feeding them into the model. Duck 2 handles this automatically if you're using the provided Python API, but if you're integrating it into a C++ application or a custom pipeline, you'll need to handle the resize and normalization yourself. The normalization constants are mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225], which matches the standard ImageNet preprocessing. Deviating from these values will noticeably drop your accuracy. Here's a minimal inference example:

Code: from duck2 import DuckModel model = DuckModel(variant='base', device='cuda')

results = model.infer('path/to/image.jpg') for detection in results.detections:   print(f"{detection.species}: {detection.confidence:.2f}")

Common Pitfalls and Edge Cases

I ran into a significant issue last autumn during a goose migration survey. The model was misclassifying immature plumage birds as a different species nearly 30% of the time. Young Canada Geese, for example, get labeled as Greylag Geese fairly often because the training data underrepresents juvenile specimens. The fix wasn't in the model weights — it was in the post-processing layer. I added a seasonal confidence modifier that lowers the threshold for species in their molt period and flags low-confidence detections for manual review instead of discarding them outright. Another thing that catches people off guard: Duck 2 struggles with backlit shots. If the sun is behind the subject, which happens constantly in early morning and late afternoon surveys, the confidence scores drop across the board. Not just for ducks — every species in the frame. I started using HDR brackets in my camera setup and feeding all three frames into the model, then voting on the classification. That cut my false negative rate by about 60%. There's also a known issue with water reflection confusion. The model occasionally detects phantom birds in rippling water, especially on overcast days when the surface acts like a mirror. Enabling the temporal consistency filter in the config helps — it requires a detection to appear in at least three consecutive frames before it's counted. This adds about 150ms of latency but eliminates roughly 90% of reflection artifacts.

When Duck 2 Isn't the Right Tool

If you're working in tropical regions with species outside the training set, the default model won't help you much. The confidence scores will look reasonable even when the classification is wrong, which is worse than getting no detection at all. In those cases, you either need to fine-tune on local species data or switch to a more general-purpose detector like YOLOv8 combined with a bird-specific classifier head. Similarly, if you need anatomical detail — wing span measurement, feather condition scoring, behavioral analysis — Duck 2 isn't built for that. It's a detection and classification tool, not a measurement platform. Pair it with a separate pose estimation model if you need that level of detail. The model also doesn't handle audio input. Some competitors in this space offer multi-modal detection that combines sound and image data. Duck 2 is vision-only. If you're in an environment where visual range is limited but auditory detection would work well, you'll need to supplement this with a separate audio classification pipeline.

Performance Benchmarks

On an NVIDIA RTX 4060, the base variant runs at approximately 120 FPS on 640x640 input. The large variant drops to about 45 FPS. On CPU-only systems, expect 8-12 FPS with the base model, which is barely sufficient for near-real-time work but perfectly fine for batch processing recorded footage. Memory usage sits around 2.1GB for the base variant and 5.8GB for the large one. If you're running multiple instances in parallel — say, processing footage from several cameras simultaneously — the tiny variant at 12MB is worth considering even though you lose some accuracy. The tradeoff usually pays off when you're managing four or more concurrent streams.

Download and Resources

The repository and pre-trained weights are available on GitHub under the Sapiens AI organization. Documentation covers the Python API, the REST endpoint for batch processing, and the configuration reference for tuning detection thresholds. There's also a Colab notebook if you want to test the model before committing to a local setup. The community is small but active. The issue tracker on GitHub tends to have answers to most configuration questions, and the Discord channel sees regular activity from researchers using the model in the field. Not every question gets answered quickly, but the people who do respond know what they're talking about.